Event recognition systems generally implement algorithms developed from machine learning research. One branch of machine learning research is directed towards automatically recognizing and classifying data patterns. Once data is recognized and classified by a machine learning algorithm running on a computing device, the machine may make decisions based on the data patterns. For example, the data patterns may be a sequence of temporal data such as human speech. Human speech is commonly observed as a temporal sequence of sounds, i.e. in order to comprehend a sentence one commonly observes all of the audible syllables over time in the order they are uttered. A sensor may observe the speech over a time period and an algorithm may then process the speech to a machine recognizable state. Once the speech is recognized, the machine may then take action that corresponds to the speech. A further example includes the recognition and classification of temporal human behavior such as gestures, sign language, walking, jumping and other human activities.
Various techniques are utilized for event recognition related to temporal sequence recognition. Specifically, graphical models such as, for example, Hidden Markov Models and Conditional Random Fields may be utilized in temporal sequence recognition. Although these graphical models have been successful in recognizing temporal sequences, they are sensitive to training parameters and require a large amount of data and processing power to recognize complex temporal sequences.
Accordingly, a need exists for alternative systems and methods for event recognition, such as temporal sequence recognition.